Methods, Systems and Devices for Detecting Atrial Fibrillation

ABSTRACT

Disclosed herein are various embodiments of methods, systems and devices for detecting atrial fibrillation (AF) in a patient. According to one embodiment, a patient places his or her left and right hands around left and right electrodes and a hand-held atrial fibrillation detection device acquires an electrocardiogram (ECG) from the patient over a predetermined period of time such as, by way of example, one minute. After acquiring the ECG from the patient, the device processes and analyzes the ECG and makes a determination of whether the patient has AF. The device may further be configured to provide a visual or audio indication of whether the patient has AF, or does not have AF. The device may be employed in a health care provider&#39;s office without the need for complicated or expensive diagnostic equipment, and is capable of providing an on-the-spot and low-cost diagnosis of AF. The device may further be connected to a physician&#39;s computer in the office, which may be configured to store the results of the analysis and the patient&#39;s ECG, and which may further be configured to carry out additional processing and analyses of the acquired and processed data. Moreover, the physician&#39;s computer may be operably connected to a remote server configured to store, process and analyze the ECG and the results provided by the hand-held device.

FIELD

Various embodiments described herein relate to the field of detectingcardiac arrhythmias in patients, and methods, components, devices andsystems therefor.

BACKGROUND

Atrial fibrillation (or AF) is the most common cardiac arrhythmia orabnormal heart rhythm suffered by human patients. AF develops in the twoupper chambers or atria of the heart, and is so-named owing to thefibrillation or quivering of the heart muscles of the atria (as opposedto the normal coordinated contraction of the atria). Patients sufferingfrom AF often have heartbeats that do not occur at regular intervals, orthey may present an absence of normal P-waves in theirelectrocardiograms (ECGs). The risk of AF increases with age, and it isestimated that 8% of people over 80 suffer from AF.

In AF, the normal electrical impulses generated by the sino-atrial (SA)node are overwhelmed by disorganized electrical impulses that originatein the atria and pulmonary veins, leading to the conduction of irregularimpulses to the ventricles that generate a heartbeat. The result is anirregular heartbeat, which may occur in episodes lasting from minutes toweeks, or that may occur continuously over a period of years. Atrialfibrillation has a pronounced tendency AF to become chronic, which inturn leads to an increased risk of severe health consequences such ascerebrovascular accident (CVA, or stroke) and death.

Atrial fibrillation is often asymptomatic, and in the general case isnot life-threatening. Atrial fibrillation can result in palpitations,fainting, chest pain, congestive heart failure, and a generallydecreased quality of life. Patients suffering from AF usually have asignificantly increased risk of stroke (up to seven times that of thegeneral population). Stroke risk increases during AF because blood maypool and form clots in the poorly contracting atria, especially in theleft atrial appendage (LAA). Atrial fibrillation is known to be aleading cause of stroke.

Detecting or diagnosing AF in a patient typically requires theacquisition, processing and analysis of ECGs from the patient, which inturn usually involves the employment of complicated, costly andspecialized medical equipment. Such medical equipment is oftenunavailable to or too costly for general practitioners and health careproviders. Moreover, such medical equipment must often be operated bymedical specialists, which further decreases the possibility ofwidespread and effective diagnosis of AF among the general population.Given the prevalence of AF in the general population, and the seriousand debilitating consequences of AF, what is needed is a more economic,easier and quicker means of diagnosing AF in patients, especially in thecontext of patients visiting their general practitioner or health careprovider.

SUMMARY

In one embodiment, there is provided a method of detecting atrialfibrillation in an electrocardiogram (ECG) acquired from a patientcomprising determining times corresponding to R-waves in theelectrocardiogram, determining a plurality of sequentially-ordered R-Rtime intervals corresponding to the R-wave times, selecting an R-R testinterval (INT) from among the plurality of R-R time intervals,sequentially selecting the R-R time intervals and comparing same in abase rhythm recognition state machine to determine which of the selectedR-R time intervals correspond to at least one of a predetermined numberof non-atrial-fibrillation states, at least some of thenon-atrial-fibrillation states requiring updating of INT when R-R timeintervals are compared therein, and further determining which of theselected R-R time intervals correspond to a potential atrialfibrillation state; generating, on the basis of the selected andcompared R-R time intervals, a base cardiac rhythm score.

According to another embodiment, there is provided a device configuredto detect atrial fibrillation in a patient comprising first and secondelectrodes configured to sense electrocardiograms (ECGs) of the patient,amplifier circuitry configured to receive and amplify the ECGs, at leastone processor configured to detect times corresponding to R-waves in theECGs, determine sequentially-ordered R-R time intervals corresponding tothe R-wave times, select an R-R test interval (INT) from among theplurality of R-R time intervals, sequentially select the R-R timeintervals and compare same in base rhythm recognition state machine todetermine which of the selected R-R time intervals correspond to atleast one of a predetermined number of non-atrial-fibrillation states,at least some of the non-atrial-fibrillation states requiring updatingof INT when R-R time intervals are compared therein, determine which ofthe selected R-R time intervals correspond to a potential atrialfibrillation state, and generate, on the basis of the selected andcompared R-R time intervals, a base cardiac rhythm score.

Further embodiments are disclosed herein or will become apparent tothose skilled in the art after having read and understood thespecification and drawings hereof.

BRIEF DESCRIPTION OF THE DRAWINGS

Different aspects of the various embodiments of the invention willbecome apparent from the following specification, drawings and claims inwhich:

FIG. 1 shows one embodiment of a hand-held atrial fibrillation detectiondevice;

FIG. 2 shows a functional block diagram according to one embodiment ofan atrial fibrillation detection device;

FIG. 3 shows a block circuit diagram according to one embodiment ofatrial fibrillation detection device;

FIG. 4 shows further details according to one embodiment of circuitryassociated with the hand-held atrial fibrillation detection device ofFIG. 3;

FIG. 5 shows one embodiment of an atrial fibrillation detection system;

FIG. 6 shows another embodiment of an atrial fibrillation detectionsystem;

FIG. 7 shows a schematic representation of combining ECG data acquiredfrom a patient by hand-held atrial fibrillation detection device withother data associated with the patient;

FIGS. 8-13 show various embodiments hand-held atrial fibrillationdetection devices;

FIG. 14 shows one embodiment of a high-level method for detecting atrialfibrillation in a patient using an atrial fibrillation detection device;

FIG. 15 shows details according to one embodiment of determining R-Rintervals;

FIG. 16 show portions of methods for detecting R-waves;

FIG. 17 shows one embodiment of R-R interval preprocessing incombination with an AF detection block/circuitry;

FIGS. 18( a) and 18(b) illustrate the results of a method ofpre-processing R-R intervals;

FIG. 19 illustrates one embodiment of a block diagram or circuitryemployed to remove R-R interval trends from R-R intervals.

FIG. 20 shows further details according to one embodiment of determiningan AF score on the basis of R-R intervals;

FIG. 21 illustrates one embodiment of a an episode base rhythmrecognition state machine;

FIGS. 22-30 illustrate examples of chains and chain lengths generated bythe base rhythm recognition state machine of FIG. 19 according tovarious patient cardiac conditions;

FIG. 31 shows further steps according to one embodiment for calculatingperiodicity and/or variability scores;

FIG. 32 shows one embodiment of autocorrelation block diagram orcircuitry;

FIG. 33 shows one embodiment of a block diagram or circuitry forprocessing autocorrelation data;

FIG. 34 shows an illustrative example of calculating a periodicity orvariability score;

FIGS. 35 and 36 illustrate one embodiment of a further method forcalculating a periodicity score.

FIG. 37 shows one example of a series of R-waves detected in a patient'sECG during an episode of normal cardiac sinus rhythm (NSR), and the R-Rinterval sequence and autocorrelation function corresponding thereto,and

FIG. 38 shows one example of a series of R-waves detected in a patient'sECG during an episode of paroxysmal atrial fibrillation, and the R-Rinterval sequence and autocorrelation function corresponding thereto;

The drawings are not necessarily to scale. Like numbers refer to likeparts or steps throughout the drawings, unless otherwise noted.

DETAILED DESCRIPTIONS OF SOME EMBODIMENTS

In the following description, specific details are provided to impart athorough understanding of the various embodiments of the invention. Uponhaving read and understood the specification, claims and drawingshereof, however, those skilled in the art will understand that someembodiments of the invention may be practiced without hewing to some ofthe specific details set forth herein. Moreover, to avoid obscuring theinvention, some well known methods, processes and devices and systemsfinding application in the various embodiments described herein are notdisclosed in detail.

In the drawings, some, but not all, possible embodiments areillustrated, and further may not be shown to scale.

FIG. 1 shows one embodiment of a hand-held atrial fibrillation detectiondevice 10 comprising first electrode 12, second electrode 14, housing16, and light pipe 18 having visual indicator 20 (not shown in FIG. 1)disposed therewithin. Device 10 is configured for a patient to holdfirst electrode 12 in one hand and second electrode 14 in the otherhand. Electrodes 12 and 14 are separated by device housing 16, which inone embodiment comprises electrically non-conductive material thatprevents the sensing of ECGs from being adversely affected. Electrodes12 and 14 may be formed of any suitable electrically conductive materialsuch as metal or a metal alloy. While the patient holds device 10, thedevice acquires electrocardiogram (ECG) data from the patient throughthe first and second electrodes 12 and 14 over a predetermined period oftime (e.g., 60 seconds). Once device 10 has successfully acquired ECGdata from the patient, the acquired ECG data are processed and analyzedusing circuitry and electronics disposed therewithin. According to oneembodiment, and after the ECG data have been acquired, processed andanalyzed, device 10 is configured to provide a visual, audio and/ortactile feedback indication of whether a normal cardiac rhythm has beendetected on the one hand, or atrial fibrillation has been detected onthe other hand. For example, light pipe 18 of device 10 may be formed ofa transparent material such as plastic and have visual indicators suchas LEDs housed therein. If a normal cardiac rhythm in the patient hasbeen detected by device 10, green LEDs in housing 16 and light pipe 18are activated to provide a visual indication that the patient's does notsuffer from atrial fibrillation. If atrial fibrillation in the patienthas been detected by device 10, red LEDs in housing 16 and light pipe 18are activated to provide a visual indication that the patient's doessuffer from atrial fibrillation. Other visual, audio and tactilefeedback methods and devices are also contemplated, as those skilled inthe art will now appreciate.

Continuing to refer to FIG. 1, device 10 is one embodiment of the“MyDiagnostick”™ device intended to discriminate between atrialfibrillation (AF) and normal cardiac rhythms (or normal sinus rhythms)in a patient. The MyDiagnostick™ device is employed to screen largepatient populations who are at risk for AF and associated complicationslike stroke. The MyDiagnostick™ device is a portable, hand-held,low-cost device that can be used by patients and health care providerssuch as general practitioners, nurses and cardiologists. TheMyDiagnostick™ device can be used at any time and in virtually any placesimply by having a patient hold the device in both hands for apredetermined period of time until the results of the screening sessionare revealed. According to one embodiment, and as described above, atthe end of the screening session light pipe 18 of the MyDiagnostick™device turns green to indicate a normal cardiac rhythm or turns red into indicate that the patient suffers from atrial fibrillation (AF). Inaddition, device 10 can be configured for use by a patient outside aphysician's or health care provider's office. Under such circumstances,if device 10 detects AF the patient can contact his or her physician toconduct a more detailed diagnosis for AF.

Continuing to refer to FIG. 1, and according to one embodiment, device10 features no buttons or controls disposed on the exterior surfacethereof for manipulation by a patient. This is because device 10 isdedicated to performing a single diagnostic test for AF only. Device 10can be configured to switch on automatically when it is picked up from arest position by means of, for example, an accelerometer or motiondetector that activates device 10 when device 10 is moved or picked up.Moreover, device 10 can be configured to switch off automatically afterit has delivered its diagnostic results or not been moved or touched fora predetermined period of time (e.g., ten minutes) using, for example,an accelerometer or motion detector that turns off device 10 when device10 has not been moved or picked up for the predetermined period of time,or that turns device 10 off after the diagnostic results have beenprovided. In one embodiment, the time period for device 10 to render adiagnosis of AF or no AF is set at one minute or less.

Note that in one embodiment device 10 is a hand-held device, although inother embodiments device 10 may be a stationary or semi-stationarydevice.

FIG. 2 shows a functional block diagram according to one embodiment ofatrial fibrillation detection device 10. The block diagram of FIG. 2illustrates the main functional blocks that process ECGs acquired fromthe patient. ECGs sensed through electrodes 12 and 14 are received byECG amplifier circuit 22, where the ECGs are amplified and then passedon to R-wave detector 32 and AF detector 34. According to oneembodiment, user interface 19 is a visual or audio indicator of AF or noAF (as described above).

FIG. 3 shows a block circuit diagram according to one embodiment ofatrial fibrillation detection device 10. FIG. 3 provides more technicaldetail regarding device 10, which according to the embodiment shown inFIG. 3 is battery-powered. In FIG. 3, device 10 features versatile powermanagement functionality, including wake-up and power-down power/sleepmanagement circuitry 40. Analog ECG signals, sensed through the palms ofthe patient through electrodes 12 and 14 are amplified by amplifiercircuitry 22, which includes amplifier 21, band-pass filter 23 andtransconductance amplifier 28 before being passed on toanalog-to-digital converter (ADC) 26. To ensure maximum fidelity of ECGsignals in the digital domain, the analog ECG signals are amplifiedand/or attenuated to fit the 10-bit scale of ADC 26. Further processingof ECG signals is accomplished in the digital domain by processor 30,such as R-wave detection, R-R interval determination, and AF detection,more about which is said below. Processor 30 may be any one or more ofan ASIC, a controller, a micro-controller, a CPU, a processor, amicro-processor, a Peripheral Interface Controller (PIC), a digitalsignal processor (DSP), or any other suitable processing or computingdevice. The ECG data may be stored in memory 38 or any other suitablestorage medium such as volatile memory, non-volatile memory, a flashdrive or memory, a hard drive, or any other suitable memory or storagedevice for later retrieval. Processor 30 and memory 38 may also beconfigured to acquire and store diagnostic and performance data, whichmay later be employed to design or implement future productimprovements. As shown in FIG. 3, and according to one embodiment,hand-held device 10 is powered by battery 42, which may be a primary orrechargeable secondary battery. In one embodiment, battery 42 isrechargeable by inductive means when placed in a docking station 44 (notshown in FIG. 3). Battery 42 may also be recharged through connection ofa battery charging cable to device 10. When battery 42 is running low onavailable charge, device 10 can be configured to provide a low-batteryindication such as by means of an LED or audio device.

FIG. 4 shows further details according to one embodiment of circuitry 22associated with hand-held atrial fibrillation detection device 10 ofFIG. 3.

FIG. 5 shows one embodiment of an atrial fibrillation detection system17 comprising a hand-held atrial fibrillation detection device 10,docking station 44, physician computer 50, remote server 56 and internet54. Device 10 may be placed in docking station 44 for battery chargingwhen not in use by a patient, or may be placed in docking station 44 topermit downloading of ECG data that have been acquired from a patientand stored therein, where the acquired and processed data are firsttransferred to physician computer 50 by means of USB or other dataconnection 52, and then to remote server 56 by means of internet 54.Data acquired and processed by device 10 may then be stored on remoteserver 56 for later retrieval, and may also be subjected to furtherprocessing at remote server 56.

FIG. 6 shows another embodiment of an atrial fibrillation detectionsystem comprising a hand-held atrial fibrillation detection device 10, adocking station 44, and physician computer 50. As shown in FIG. 5,battery 42 of device 10 may be charged through contacts 46 and 48,and/or alternatively data stored in device 10 may be downloaded throughcontacts 46 and 48 to physician computer 50. Other embodiments andconfigurations for downloading data stored in device 10 and transferringsame to physician computer 50 are also contemplated, such as a USB datastick configured to plug into device 10, Bluetooth or other protocolwireless transfer of data between device 10 and computer 50, MICS(Medical Implant Communications Service) protocol transfer of data, andthe like.

FIG. 7 shows a schematic representation of combining ECG data acquiredfrom a patient by hand-held atrial fibrillation detection device 10 withother data associated with the patient, such as patient demographics,anti-arrhythmic medication, AF scores, and/or measurement dates andtimes. ECG data are transferred to physician computer 50 via firstinterface cable 52, while other data 60 are transferred to physiciancomputer via second interface cable 62. In physician computer 50 the ECGdata and the other data may be combined and/or analyzed as desired.

FIGS. 8-13 show various embodiments hand-held atrial fibrillationdetection devices 10. In the embodiment of hand-held atrial fibrillationdetection device 10 shown in FIG. 8, device 10 comprises an elongatecylindrically-shaped housing 16 having electrodes 12 and 14 disposed atopposite ends thereof, where electrodes 12 and 14 are configured for apatient to hold in his or her hands. Light pipe 18 is formed from asemi-transparent or transparent polymer, and permits light emitted byred and green LEDs located within housing 16 and/or light pipe 18 to betransmitted therethrough for viewing by a patient or physician, wherefor example the illumination of green LEDs indicates that no atrialfibrillation has been detected in the patient, and the illumination ofred LEDs indicates that atrial fibrillation has been detected in thepatient. The LEDs may further be configured to blink or not blink whenilluminated. As described above, other means and methods are alsocontemplated for indicating whether or not atrial fibrillation has beendetected in the patient, such as other visual displays, means andmethods, and/or audio or tactile feedback means and methods.

According to one embodiment, housing 16 ranges between about ½ inch andabout 2 and ½ inches in diameter, although other diameters arecontemplated. Moreover, housing 16 need not be cylindrically shaped, butmay assume any configuration suitable for a patient to hold and forhousing the necessary electronics therewithin.

In the embodiment of hand-held atrial fibrillation detection device 10shown in FIG. 9, device 10 comprises an elongate curved andcylindrically-shaped housing 16 having electrodes 12 and 14 disposed atopposite ends thereof, where electrodes 12 and 14 are configured for apatient to hold in his or her hands. Light pipe 18 is formed from asemi-transparent or transparent polymer, and permits light emitted byred and green LEDs located within housing 16 and/or light pipe 18 to betransmitted therethrough for viewing by a patient or physician.

In the embodiment of hand-held atrial fibrillation detection device 10shown in FIG. 10, device 10 comprises a housing 16 configured to situpright on a flat surface such as a table top, and has electrodes 12 and14 disposed at opposite ends thereof, where electrodes 12 and 14 areconfigured for a patient to hold in his or her hands. Light pipe 18 isagain formed from a semi-transparent or transparent polymer, and permitslight emitted by red and green LEDs located within housing 16 and/orlight pipe 18 to be transmitted therethrough for viewing by a patient orphysician.

In the embodiment of hand-held atrial fibrillation detection device 10shown in FIG. 11, device 10 comprises an elongate curved andcylindrically-shaped housing 16 having bulbous electrodes 12 and 14disposed at opposite ends thereof, where electrodes 12 and 14 areconfigured for a patient to hold in his or her hands. Light pipe 18 isformed from a semi-transparent or transparent polymer, and permits lightemitted by red and green LEDs located within housing 16 and/or lightpipe 18 to be transmitted therethrough for viewing by a patient orphysician.

In the embodiment of hand-held atrial fibrillation detection device 10shown in FIG. 12, device 10 comprises an elongate curved andcylindrically-shaped housing 16 having electrodes 12 and 14 disposed atopposite ends thereof, and where electrodes 12 and 14 are covered bybulbous-shaped electrically conductive electrode cover 11 andelectrically conductive electrode cover 13. Covers 11 and 13 are formedof a compressible and electrically conductive material that conforms tothe shape of the patient's hands during use.

In the embodiment of hand-held atrial fibrillation detection device 10shown in FIG. 13, device 10 comprises elongate and cylindrically-shapedhousing 16 having electrodes 12 and 14 disposed at opposite endsthereof, and where electrodes 12 and 14 are covered by bulbous-shapedelectrically conductive electrode cover 11 and electrically conductiveelectrode cover 13. Covers 11 and 13 are formed of a compressible andelectrically conductive material that conforms to the shape of thepatient's hands during use, and covers 11 and 13 are separated byelectrically insulative cover 15, which may have ports or openingsdisposed therethrough that permit the passage of light emitted by LEDsor visual indicators 20 therethrough. Note that according to oneembodiment the electronics associated with device 10 may be locatedwithin a protective pouch disposed within housing 16. To avoidunnecessary detail, note further that not all electrical and mechanicalconnections and features corresponding to device 10 are shown in theFigures.

Referring now to FIG. 14, there is shown one embodiment of high-levelmethod 100 for detecting atrial fibrillation in a patient using device10. At step 102, the patient's ECG is acquired. At step 104, the qualityof the acquired ECG is assessed by device 10. At step 106, it isdetermined whether the ECG is of suitably high quality, or not (step108). If the ECG is of sufficiently high quality, it is passed on tostep 110, where R-waves associated with the ECG are detected. At step112, and on the basis of the R-waves that have been detected by device10, atrial fibrillation in the ECGs is detected (or not). The outcome ofthe atrial fibrillation analysis of the R-waves is reported at step 114.

FIG. 15 shows a portion of method 100 of FIG. 14, and is directed inparticular to step 110 thereof (R-wave Detection). At step 116, an ECGthat has passed through step 106 of FIG. 14 as an “OK” ECG (or suitablyhigh quality ECG) is passed on to step 116 of FIG. 15 for ECGpre-processing. According to one embodiment, pre-processing of aqualified ECG is carried out to generate a “feature signal”, andcomprises band-pass filtering and differentiation of the ECG, followedby carrying out a non-linear expansion operation, and then a movingaverage operation before the pre-processed ECG is passed on to an R-wavedetection state machine at step 118. According to one embodiment,pre-processing at step 116 begins with a band-pass filtering anddifferentiating operation defined by the equation:

${y(n)} = \frac{{\sum\limits_{k = 0}^{K}\; {b_{k}{x( {n - k} )}}} - {\sum\limits_{m = 0}^{M}\; {a_{m}{y( {n - m} )}}}}{a_{0}}$

where a=[6, −9, 4] and b=[−1, −2, 1, 4, 1, −2, −1]. According to oneembodiment, and by way of non-limiting illustrative example only, theband-pass filtering and differentiating operation is followed by anon-linear expansion operation defined by the equation:

y(n)=x ²(n)

Next, a recursive moving average filter is applied to the processed ECGdata, as shown in method 125 of FIG. 16. The recursive moving averagefilter operation of FIG. 16 generates feature signal (117). Next, thepre-processed ECG data (feature signal 117 of FIG. 15) are forwarded tothe R-wave detection state machine at 118, where R-wave epochs (119) aregenerated for use in the calculation of R-R intervals (121 of FIG. 15).

According to one embodiment, during R-wave detection and classification(see peak detection state machine 118 of FIG. 15), R-waves are sortedaccording to four different states: (1) R-wave detection blanking periodtimed out; (2) R-wave detection threshold exceeded; (3) R-wave peakfraction not met; and (4) Blanked for R-wave detection. The parametersutilized for each of these R-wave categories for states (1) through (4),respectively, are: (a) K1: Fraction of the peak amplitude that thefeature signal must go below to confirm R-wave detection (b) K2:Fraction of the peak amplitude used to calculate a new detectionthreshold value; (c) THTMIN: Minimum detection threshold (mV), and (d)TBLANKING: Blanking period (msec). State 1 of the R-wave detector statemachine is initially entered each time after the R-wave detectionblanking timer times out following previous R-wave detection. In thisstate, the R-wave detector is waiting for the feature signal (117) toexceed the current threshold. If this occurs, the state machine moves tostate 2 (threshold exceeded). In this state, the maximum amplitude (i.e.the peak amplitude) of the feature signal is determined until thefeature signal goes below a fraction K1 of the peak amplitude of thefeature signal. At that moment in time, the threshold is adapted toanother fraction K2 times (*) the determined or found peak amplitude andthe state machine moves to state 3, confirming R-wave detection. AfterR-wave detection (state 3), the R-wave detection state machine movesdirectly to state 4 (Blanked for R-wave detection) and a counter isstarted that counts from a predefined value (TBLANKING) down to zero.When zero is reached, the state machine moves from state 4 back to state1, thereby restarting the detection cycle.

Referring now to FIG. 17 and FIG. 18( b), a short R-R interval in FIG.18( a) has been removed according to the equation RR<MINRR, where theR-R intervals of FIG. 18( b) conform to a rule where new R-R intervals(i.e., FIG. 18( b)) are the sum of a short interval preceding orfollowing another “normal” R-R interval. Removing R-R intervals that aretoo short reduces the effects of noise on acquired ECGs. In such amanner, the selected R-R intervals remain closest to detected meanintervals. Similarly, R-R intervals that are too long are also removed(i.e. where RR>MAXRR).

R-R intervals are further pre-processed (see 134 in FIG. 17) by removingupward or downward trends in R-R intervals (see FIG. 19) from such databy subtracting linearly interpolated R-R interval data at 166 from R-Rinterval data at step 180. After correction for mean R-R interval 174,trend-shifted R-R interval data are output at 182.

FIG. 20 shows further details according to one embodiment of step orcircuitry 112 in FIG. 14, where an AF score is developed based on R-Rintervals input thereto from step/circuitry 110. R-R intervals areanalyzed in blocks 201 (Calculate Base Rhythm Score), 203 (CalculatePeriodicity Score), 205 (Calculate Variability Score) and 207 (CalculateAutonomousness). Scores for each of steps/circuitry 201, 203, 205 and207 are then forwarded to step/circuitry 209, where an overall inferenceor AF score is calculated. The AF score indicates whether or not apatient whose ECGs have been acquired and analyzed by device 10 hasatrial fibrillation (AF). More about FIG. 20 is said below.

Referring now to FIG. 21, there is shown one embodiment illustratingcalculation of the base rhythm score by state machine 134. In FIG. 21,the illustrated state diagram implements a base rhythm recognitionalgorithm configured to detect non-AF episodes. Normal rhythms,interrupted by one or more episodes such as interpolated ornon-interpolated premature ventricular contractions (i.e., intPVC ornonintPVC) are characterized by rate increases (RR⁻) and rate decreases(RR⁺) that are recognized by moving from state to state. Thestate-machine shown in FIG. 21 remains in the same state in betweenbeats. State-transitions may be made at each R-wave detection timeinterval R(n), taking the last R-R interval RR(n)=R(n)−R(n−1) intoaccount to determine at each state (states A through G) what the nextstate will be. At all times, a test interval (INT) is defined, startingwith the first interval that can be updated during state transitions(including moving from state A and back to state A). During stablerhythms, the base rhythm recognition state machine 134 remains in stateA, symbolized by the arrow labeled RR(n)==INT, starting and ending atstate A. If the interval RR(n) ‘equals’ the test interval, the statemachine remains in state A. In this context, ‘equal’ (or ==) means thata relative or absolute deviation (K % or M msec) is allowed in aninterval RR(n) but is nevertheless still considered as being “equal.”

If a short interval is encountered where the condition RR(n)<INT istrue, state machine 134 moves from state A (138) to state B (140) (seeFIG. 22). From state B or 140, the next interval is processed. If thesum of interval RR(n) and previous interval RR(n−1) equals INT, statemachine 134 moves to state C (or 142), which is indicative of aninterpolated PVC (see FIG. 22). The test interval INT is then updated{INT=RR(n−1)+RR(n)}. If however, while in state B the next interval‘equals’ INT, the state machine moves to state D (non-interpolated PVC).The test interval INT is then updated {INT=RR(n)}, (see FIG. 23). Ifwhile in state C or state D the new interval RR(n) is again too short,the state machine moves back to state B. Doublet ECG signals, tripletECG signals and (short) VT ECG signals then become state sequences asshown in FIGS. 24, 25 and 26. A normal sinus rhythm followed by a lowerrate ECG signal initiates an A-state sequence followed by states F, G(see FIG. 27). Various combinations of normal sinus rhythms, PVCs, rateincreases and decreases can be coded by chains of state transitions (seeFIGS. 28, 29 and 30). Non-facilitated or unrecognized R-R sequences leadto breaking such chains at 152, 154, 156, 158, 160 or 162) and a restartof the state machine at state A. In such a manner, evaluation of asequence of R-R intervals corresponding to the patient's acquired ECGsignals creates one or more chains of state machine data of variablelength.

Stable cardiac rhythms interrupted by expected patterns such as PVCs,rate increases and rate decreases create a long single chain of statemachine data. In contrast, variable cardiac rhythms create more andshorter chains of state machine data. A low number of long chains areindicative of the presence of a base rhythm. On the other hand, a highnumber of short chains indicate the presence of a base rhythmrepresentative of AF (or an AF condition for the patient). During AF,short multiple chains (e.g., 15-30 chains) consisting of 5-10 beats aretypically found.

Still referring to FIG. 17, R-R intervals provided by x(n)−x(n−1) statemachine 120 in FIG. 15 are provided as inputs to the base rhythm statemachine 134. As shown in FIG. 21, R-R intervals are first input to stepA (Fit) to determine whether a new R-R interval (RR(n)==INT) is ‘equal’to a previously analyzed R-R interval or not. In this context, ‘equal’means equal within a range of acceptance, i.e., equal to K % of normalrelative or M msec absolute variation due to autonomic regulation.Typically, K is 10%, but this figure may be adapted to optimize theallowed RR variation per patient during episodes of normal sinus rhythm.If the new R-R interval is greater than the test interval (INT), thestate machine goes from state A to state B or 140 (“Too Short”). If thenew R-R interval is less than the test interval (INT), the new R-Rinterval is sent to state F or 148 (“Too Long”). At steps 140 and 148,the newly presented R-R interval is analyzed once again to determinewhether it is greater than or less than the test interval (INT). If thenew R-R interval at step 148 is equal to the test interval (INT), theanalysis is terminated at step 168. If the new R-R interval at step 148is greater than the test interval (INT), the analysis continues at step150 (G or “RR⁺), where a determination is made as to whether (R-R(n)) isgreater than (INT) (at which point further analysis is terminated) orless than INT (at which point further analysis is also terminated). Ifthe new R-R interval (RR(n)) at step 140 is greater than the testinterval (INT), the analysis is terminated at step 152. If the new R-Rinterval (RR(n)) at step 140 is less than the test interval (INT), theanalysis continues at step 146 (E or RR⁻), where a determination is madeas to whether RR(n) is greater than (INT) (at which point furtheranalysis is terminated at step 160) or less than INT (at which pointfurther analysis is also terminated at step 158). If the new R-Rinterval RR(n) at step 140 is exactly equal to the previously analyzedRR interval INT, the analysis continues at step 144 (D or Nonint PVC),where a determination is made as to whether RR(n) is greater than (INT)(at which point further analysis is terminated at step 156) or less thanINT (at which point analysis is continued at step 138 (A or Fit).Moreover, if at step 140 the sum of the new R-R interval (RR(n)) and thepreviously analyzed interval (RR(n−1)) is exactly equal to INT, theanalysis continues at step 142 (C or Int PVC), where a determination ismade as to whether such sum is greater than (INT) (at which pointfurther analysis is terminated at step 154) or less than INT (at whichpoint analysis is continued at step 138 (A or Fit). Base rhythm statemachine 134 thus arrives at determinations of which R-R intervalsindicate the lack of presence of a base rhythm characteristic of atrialfibrillation.

According to one embodiment of methods and devices corresponding to theBase Rhythm State Machine of FIG. 21, provided below is computerpseudo-code corresponding thereto.

TABLE 1 Computer Pseudo-Code Corresponding to the Operation of a OneEmbodiment of a Base Rhythm State Machine % CALL %[chain,type]=findbaseRhythm_sm(RR,M) % % DESCRIPTION % RR RR intervalvector (ms) % M Range of allowance of beat to beat variation (fraction)% PLOT Plot on/off % % chain Array of cells of indices chained intervals% type Array of cells of types of chained intervals % % IDENTIFICATION %Richard Houben, Applied Biomedical Systems BV % function[chain,type]=findbaseRhythm_state(RR,M,PLOT) % 1. Initialize chainNum=1;% Chain number chainIdx=1; % Chain indexchain{chainNum,chainIdx}=1;type{chainNum,chainIdx}=1;chainIdx=chainIdx+1; n=2; % RR interval index INT=RR(1); % Test intervalaINT(1)=INT; state=1; while 1 % Termination condition if PLOT;aINT(n)=INT; end if n>length(RR); break ; end switch state case 1 % Fitif EQ(RR(n),INT,M) state=1; INT=RR(n); elseif LT(RR(n),INT,M) state=2;elseif GT(RR(n),INT,M) state=6; end case 2 % Too Short ifEQ(RR(n−1)+RR(n),INT,M) state=3; INT=RR(n−1)+RR(n); elseifEQ(RR(n),INT,M+0.1) state=4; INT=RR(n); elseif LT(RR(n),INT,M) state=5;INT=RR(n); elseif GT(RR(n),INT,M) state=−2; end case 3 % PVCi ifLT(RR(n),INT,M) state=2; elseif EQ(RR(n),INT,M) state=1; elseifGT(RR(n),INT,M) state=−3; end case 4 % PVCni if LT(RR(n),INT,M) state=2;elseif EQ(RR(n),INT,M) state=1; elseif GT(RR(n),INT,M) state=−4; endcase 5 % RR− if EQ(RR(n),INT,M) state=1; elseif GT(RR(n),INT,M)state=−5; elseif LT(RR(n),INT,M) state=−5; end case 6 % Too long ifEQ(RR(n),INT,M) state=−6; elseif GT(RR(n),INT,M) state=7; INT=RR(n);elseif LT(RR(n),INT,M) state=−6; end case 7 % RR+ if EQ(RR(n),INT,M)state=1; elseif LT(RR(n),INT,M) state=−7; elseif GT(RR(n),INT,M)state=−7; end end % Update chain if state>0 % Add to chainchain{chainNum,chainIdx}=n; type{chainNum,chainIdx}=state;chainIdx=chainIdx+1; n=n+1; else % Terminate chainchain{chainNum,chainIdx}=n; type{chainNum,chainIdx}=state; % Initiatenew chain state=1; chainNum=chainNum+1; chainIdx=1;chain{chainNum,chainIdx}=n; type{chainNum,chainIdx}=state;chainIdx=chainIdx+1; n=n+1;if n>length(RR); break ; end INT=RR(n); endend function k=EQ(RR,INT,M); k=(RR>=INT*(1−M) & RR<=INT*(1+M)); functionk=GT(RR,INT,M); k= RR>(INT*(1+M*2)); function k=LT(RR,INT,M); k=RR<(INT*(1−M));

FIG. 31 shows further steps according to one embodiment for calculatingvariability and/or periodicity scores, where linearized, corrected ortrend-shifted R-R interval data are input to step 184 and a vector ofones and zeros is generated with W ones following each detected R-waveepoch using a W-tap finite impulse response filter (FIR, 188) togenerate an output Rfilter. FIG. 32 shows an autocorrelationstep/circuitry 194, which auto-correlates the Rfilter data to providecorrelated output data 198, which are calculated in steps of W+1 samplesover N steps using the Rfilter data as inputs. Next, and as shown inFIG. 33, in one embodiment correlated output data 198 are input tomethod/circuitry 199 to generate a calculated R-R variability score(RRvar) as a fraction of time the correlation function exceeds aMAXCORRFRAC of the maximum correlation value that is determined. Anillustrative example of calculating a periodicity or variability scoreis shown in FIG. 34, where output CorrGT (the output provided bymethod/circuitry 199 of FIG. 33) corresponds to those portions of Correxceeding CorrT. Stable cardiac rhythms create a peak R-Rautocorrelation function (Corr) occasionally exceeding CorrT, whereasvariable cardiac rhythms such as AF create a more continuous decayingR-R autocorrelation function with more and longer portions exceedingCorrT and therefore a higher RRvar (see 216 in FIG. 33).

FIGS. 35 and 36 illustrate one embodiment of a further method forcalculating a periodicity score. CorrGT of a periodic RR intervalsequence, related to normal sinus rhythm shows short portions of Correxceeding CorrT separated by longer non-correlating episodes, whereCorr≦CorrT. In FIG. 35, the number of consecutive of zeros (Corr≦CorrT)in CorrGT (consZeros) are counted. Also, weight values (consWeight) thattaper down for episodes corresponding to zeroes found at largercorrelation lags are created. In FIG. 36, sequences of consecutivezeroes shorter than MINCONZEROSPERIOD are removed, before the quantityRRper is calculated as a function of the fraction of remainingconsecutive zeros (consZeros) times (*) weightZeros (237) and thesequence of N samples in the sequence.

Referring to FIG. 20 (207), according to one embodiment theautonomousness score is calculated based on the R-R intervals byquantifying the amount of heart rate variability specifically induced bythe respiratory system and baroreflex, which is one of the body'shomeostatic mechanisms for maintaining blood pressure that can beobserved during normal sinus rhythm. In one embodiment, power spectralanalysis is used to calculate the presence and amount of the respiratoryartifact within the frequency band from 0.2-0.4 Hz, reflecting breathingrate and the baro-reflex, which typically peak between 0.05-0.15 Hz. Inanother embodiment, time domain heart rate variability methods anddeceleration capacity are used to analyze R-R sequences quantifying theinfluence of the autonomic nerve system that can be observed duringnormal sinus rhythm but not during AF.

Next, and according to one embodiment, the AF score is generatedaccording to the equation:

AFScore=(3*variabilityScore−periodicyScore−2*baseRhythmScore)/3

The resulting AFScore is compared against a predetermined threshold toarrive at a final determination of whether or not the patient has AF.According to one embodiment, such a predetermined threshold rangesbetween about −1.0 and about 1.0. Typically, and in one embodiment, anAF threshold value of 0.15 is used. A lower threshold makes detectionmore sensitive at the expense of specificity, and vice-versa. If theAFScore is below the predetermined threshold, then an AF episode has notbeen detected, and the patient is deemed not to have AF. If the AFScoreis above the predetermined threshold, then an AF episode has beendetected, and the patient is determined to have AF. Note that fuzzylogic, artificial neural networks (ANN), support vector machines (SVM)and other computational methods may be employed to arrive at such afinal determination of AF or No AF.

Referring now to FIG. 37, there is shown in the upper panel thereof oneexample of a series of R-waves detected in a patient's ECG during anepisode of normal cardiac sinus rhythm (NSR). In the lower left-handpanel there is shown the corresponding R-R interval sequence, while inthe lower right-hand panel there is shown the correspondingautocorrelation function of the R sequence (Rfilter) that has beenobtained after the R-R sequence has been regularized or linearized, andupward or downward R-R trends have been removed from the R-R intervals.As shown in FIG. 37, the variability score is 0.07 and the periodicityscore is 0.69. The resulting AF score is 0.10, which is below an AFthreshold of 0.15).

FIG. 38 shows a different example, where the upper panel thereofcontains R-waves detected in a patient's ECG during an episode ofparoxysmal atrial fibrillation. In the lower left-hand panel there isshown the corresponding R-R interval sequence, while in the lowerright-hand panel there is shown the corresponding autocorrelationfunction of the R sequence (Rfilter) that has been obtained after theR-R sequence has been regularized or linearized, and upward or downwardR-R trends have been removed from the R-R intervals. In the example ofFIG. 38, the resulting variability score is 0.76 and the periodicityscore is 0.22. The resulting AF score is 3.53, which is well above theAF threshold of 0.15.

Referring now to the preceding text and diagrams, it will be that thereare disclosed and described various embodiments of methods and devicesfor detecting atrial fibrillation in an electrocardiogram (ECG) acquiredfrom a patient, where times corresponding to R-waves in theelectrocardiogram are determined, a plurality of sequentially-orderedR-R time intervals corresponding to the R-wave times are determined, anR-R test interval (INT) is selected from among the plurality of R-R timeintervals, R-R time intervals are sequentially selected and compared ina base rhythm recognition state machine to determine which of theselected R-R time intervals correspond to at least one of apredetermined number of non-atrial-fibrillation states, and at leastsome of the non-atrial-fibrillation states require updating of INT whenR-R time intervals are compared therein. Next, it is determined which ofthe selected R-R time intervals correspond to a potential atrialfibrillation state, and on the basis of the selected and compared R-Rtime intervals, a base cardiac rhythm score is generated.

The predetermined number of non-atrial-fibrillation states may includeat least one of a no-change state, a premature beat state, aninterpolated premature ventricular contraction state, a non-interruptedpremature ventricular contraction state, a faster rate change state, aslower rate change state, and a pause state. The R-R time intervals maybe compared to INT to determine which of the selected R-R time intervalscorresponds to at least one of a predetermined number ofnon-atrial-fibrillation states using a comparison threshold rangingbetween about 90% of INT and about 110% of INT when comparing each R-Rtime interval to INT. Determining times corresponding to R-waves in theECG may also further comprise at least one of band-pass filtering anddifferentiation of the ECG, non-linear expansion filtering of the ECG,moving average filtering of the ECG, and using an R-peak detection statemachine. Determining the plurality of sequentially-ordered R-R timeintervals corresponding to the R-wave times may further comprisesubtracting a first time marker for one R-wave from a second time markerfor another R-wave. Generating the base cardiac rhythm score may furthercomprise detecting at least one of episodes of atrial fibrillation andepisodes of non-atrial fibrillation on the basis of the selected andcompared R-R time intervals. The plurality of sequentially-ordered R-Rtime intervals may be further processed to at least one of regularizethe plurality of sequentially-ordered R-R time intervals, remove upwardtrends in the plurality of sequentially-ordered R-R time intervals,remove downward trends in the plurality of sequentially-ordered R-R timeintervals, and generate an R-sequence function based on thesequentially-ordered R-R time intervals. As described above, thesequentially-ordered R-R time intervals may be auto-correlated, a rateestimate based on the sequentially-ordered R-R time intervals may becalculated, an R-R variability score based on the sequentially-orderedR-R time intervals may be calculated, and an R-R periodicity score basedon the sequentially-ordered R-R time intervals may also be calculated.

The base cardiac rhythm score may be combined with at least one of theR-R variability score, R-R periodicity score and autonomousness score toproduce an atrial fibrillation evidence score. On the basis of theatrial fibrillation evidence score it may be determined whether or notthe patient has atrial fibrillation. The foregoing methods may also becarried out using a hand-held device, and the hand-held device maycomprise first and second electrodes configured to sense the ECGs of thepatient. The device may be configured to provide an audio or visualindication that the patient has atrial fibrillation, or does not haveatrial fibrillation, after the patient's ECG has been acquired andanalyzed by the device.

The components, devices, systems and methods described above may beimplemented in medical diagnostic and therapeutic devices other than thespecific external embodiments illustrated, for example, in FIGS. 1through 13, and may be implemented in implantable medical devices suchas pacemakers, implantable cardioverters (ICDs), implantable heartmonitors or diagnostic devices, implantable loop recorders, externalcardiac monitors or diagnostic devices other than those describedexplicitly above, and other components, devices, systems and methodsthat will become apparent to those skilled in the art upon having readand understood the present specification and drawings.

In some such additional embodiments, functionality similar to thatdepicted in FIG. 3, where electrodes 12 and 14 may be employed that aresubstantially equivalent or similar to external electrodes or electrodesincluded in implantable electrical leads.

Application of the above-described components, devices, systems andmethods may be especially useful when atrial cardiograms are difficultto obtain and/or when only ventricular cardiograms are available foranalysis. Examples of devices that are typically not configured toobtain atrial cardiograms are implantable loop recorders (ILR),implantable ventricular and bi-ventricular pacemakers, implantablecardioverters and defibrillators, external ventricular pacemakers,external loop recorders, and external defibrillators, and that may bemodified in accordance with the teachings presented herein.

Examples of devices that typically are not configured to recordelectrical cardiac signals, but which measure or derive cardiac activityfrom other physiological sources are blood-pressure measuringinstruments and devices, plethysmogram-based devices, impedancemeasuring instruments and devices, electronic stethoscope, andultra-sound instruments and devices, which may be implantable orconfigured for external use.

The components, devices, systems and methods described above permit thepresence or absence of atrial fibrillation to be determined on the basisof ventricular activity, and may be implemented in any of the foregoingdevices or systems. Furthermore, and in accordance with the teachingspresented herein, atrial fibrillation may be detected using any suitablesource of ventricular activity information such as, by way ofnon-limiting example only, subsequent ventricular events or datasets ofventricular intervals provided by any communication means or stored indatabases, and which can be processed locally or remotely to determinethe presence, absence or degree of atrial fibrillation.

The above-described embodiments should be considered as examples of thepresent invention, rather than as limiting the scope of the invention.In addition to the foregoing embodiments of the invention, review of thedetailed description and accompanying drawings will show that there areother embodiments of the invention. Accordingly, many combinations,permutations, variations and modifications of the foregoing embodimentsof the invention not set forth explicitly herein will nevertheless fallwithin the scope of the invention.

1. A method of detecting atrial fibrillation in an electrocardiogram(ECG) acquired from a patient, comprising: determining timescorresponding to R-waves in the electrocardiogram; determining aplurality of sequentially-ordered R-R time intervals corresponding tothe R-wave times; selecting an R-R test interval (INT) from among theplurality of R-R time intervals; sequentially selecting the R-R timeintervals and comparing the R-R intervals in an episode base rhythmstate machine to determine which of the selected R-R time intervalscorrespond to at least one of a predetermined number ofnon-atrial-fibrillation states, at least some of thenon-atrial-fibrillation states requiring updating of INT when R-R timeintervals are compared therein, and further determining which of theselected R-R time intervals correspond to a potential atrialfibrillation state, and generating, on the basis of the selected andcompared R-R time intervals, a base cardiac rhythm score.
 2. The methodof claim 1, wherein the predetermined number of non-atrial-fibrillationstates includes at least one of a no-change state, a premature beatstate, an interpolated premature ventricular contraction state, anon-interrupted premature ventricular contraction state, a faster ratechange state, a slower rate change state, and a pause state.
 3. Themethod of claim 1, wherein comparing the R-R time intervals to INT todetermine which of the selected R-R time intervals corresponds to atleast one of a predetermined number of non-atrial-fibrillation statesfurther comprises using a comparison threshold ranging between about 90%of INT and about 110% of INT when comparing each R-R time interval toINT.
 4. The method of claim 1, wherein determining times correspondingto R-waves in the ECG further comprises at least one of band-passfiltering and differentiation of the ECG, non-linear expansion filteringof the ECG, moving average filtering of the ECG, and using an R-peakdetection state machine.
 5. The method of claim 1, wherein determiningthe plurality of sequentially-ordered R-R time intervals correspondingto the R-wave times further comprises subtracting a first time markerfor one R-wave from a second time marker for another R-wave.
 6. Themethod of claim 1, wherein generating the base cardiac rhythm scorefurther comprises detecting at least one of episodes of atrialfibrillation and episodes of non-atrial fibrillation on the basis of theselected and compared R-R time intervals.
 7. The method of claim 6,wherein the plurality of sequentially-ordered R-R time intervals arefurther processed to at least one of regularize the plurality ofsequentially-ordered R-R time intervals, remove upward trends in theplurality of sequentially-ordered R-R time intervals, remove downwardtrends in the plurality of sequentially-ordered R-R time intervals, andgenerate an R-sequence function based on the sequentially-ordered R-Rtime intervals.
 8. The method of claim 1, further comprising at leastone of auto-correlating the sequentially-ordered R-R time intervals,calculating a rate estimate based on the sequentially-ordered R-R timeintervals, calculating an R-R variability score based on thesequentially-ordered R-R time intervals, and calculating an R-Rperiodicity score based on the sequentially-ordered R-R time intervals.9. The method of claim 8, further comprising combining the base cardiacrhythm score with at least one of the R-R variability score and the R-Rperiodicity score to produce an atrial fibrillation evidence score. 10.The method of claim 1, further comprising determining on the basis ofthe atrial fibrillation evidence score whether or not the patient hasatrial fibrillation.
 11. The method of claim 1, wherein the method ofdetecting atrial fibrillation is carried out using a hand-held device.12. The method of claim 11, wherein the hand-held device comprises firstand second electrodes configured to sense the ECGs of the patient. 13.The method of claim 1, wherein the method of detecting atrialfibrillation is carried out using an implantable medical device.
 14. Themethod of claim 13, wherein the implantable medical device is one of apacemaker, an implantable cardioverter (ICD), an implantable looprecorder, and an implantable cardiac monitor.
 15. The method of claim 1,wherein the first and second electrodes form a portion of at least oneimplantable medical electrical lead.
 16. The method of claim 1, furthercomprising determining that the patient has atrial fibrillation.
 17. Themethod of claim 16, further comprising providing one of an audioindication and a visual indication that the patient has atrialfibrillation.
 18. The method of claim 1, further comprising determiningthat the patient does not have atrial fibrillation.
 19. The method ofclaim 18, further comprising providing one of an audio indication and avisual indication that the patient does not have atrial fibrillation.20. (canceled)
 21. The method of claim 1, wherein the method ofdetecting atrial fibrillation is carried out using a hand-held device,the hand-held device acquires the ECG from the patient, and a computerprocesses the ECG to determine whether the patient has atrialfibrillation.
 22. The method of claim 21, wherein the computer is aremote computer.
 23. A device configured to detect atrial fibrillationin a patient, comprising: first and second electrodes configured tosense electrocardiograms (ECGs) of the patient; amplifier circuitryconfigured to receive and amplify the ECGs; at least one processorconfigured to detect times corresponding to R-waves in the ECGs,determine sequentially-ordered R-R time intervals corresponding to theR-wave times, select an R-R test interval (INT) from among the pluralityof R-R time intervals, sequentially select the R-R time intervals andcompare the R-R intervals in an episode base rhythm state machine todetermine which of the selected R-R time intervals correspond to atleast one of a predetermined number of non-atrial-fibrillation states,at least some of the non-atrial-fibrillation states requiring updatingof INT when R-R time intervals are compared therein, determine which ofthe selected R-R time intervals correspond to a potential atrialfibrillation state, and generate, on the basis of the selected andcompared R-R time intervals, a base cardiac rhythm score.
 24. The deviceof claim 23, wherein the predetermined number of non-atrial-fibrillationstates includes at least one of a no-change state, a premature beatstate, an interpolated premature ventricular contraction state, anon-interrupted premature ventricular contraction state, a faster ratechange state, a slower rate change state, and a pause state.
 25. Thedevice of claim 23, wherein the at least one processor is furtherconfigured to use a comparison threshold ranging between about 90% ofINT and about 110% of INT when comparing each R-R time interval to INTto determine which of the selected R-R time intervals corresponds to atleast one of a predetermined number of non-atrial-fibrillation states.26. The device of claim 23, wherein the at least one processor isfurther configured to at least one of band-pass filter and differentiatethe ECG, non-linear expansion filter the ECG, moving average filter theECG, and use an R-peak detection state machine.
 27. The device of claim23, wherein the at least one processor is further configured to subtracta first time marker for one R-wave from a second time marker for anotherR-wave.
 28. The device of claim 23, wherein the at least one processoris further configured to generate the base cardiac rhythm score bydetecting at least one of episodes of atrial fibrillation and episodesof non-atrial fibrillation on the basis of the selected and compared R-Rtime intervals.
 29. The device of claim 23, wherein the at least oneprocessor is further configured to process the plurality ofsequentially-ordered R-R time intervals to at least one of regularizethe plurality of sequentially-ordered R-R time intervals, remove upwardtrends in the plurality of sequentially-ordered R-R time intervals,remove downward trends in the plurality of sequentially-ordered R-R timeintervals, and generate an R-sequence function based on thesequentially-ordered R-R time intervals.
 30. The device of claim 23,wherein the at least one processor is further configured to at least oneof auto-correlate the sequentially-ordered R-R time intervals, calculatea rate estimate based on the sequentially-ordered R-R time intervals,calculate an R-R variability score based on the sequentially-ordered R-Rtime intervals, and calculate an R-R periodicity score based on thesequentially-ordered R-R time intervals.
 31. The device of claim 23,wherein the at least one processor is further configured to combine thebase cardiac rhythm score with at least one of the R-R variability scoreand the R-R periodicity score to produce an atrial fibrillation evidencescore.
 32. The device of claim 23, wherein the at least one processor isfurther configured to determine on the basis of the atrial fibrillationevidence score whether or not the patient has atrial fibrillation. 33.The device of claim 23, wherein the device further comprises anelongated housing having the amplifier circuitry and processor disposedtherewithin.
 34. The device of claim 23, wherein the device furthercomprises at least one of an audio device and a visual device configuredto provide an indication that the patient has atrial fibrillation. 35.The device of claim 23, further comprising at least one of an audiodevice and a visual device configured to provide an indication that thepatient does not have atrial fibrillation.
 36. The device of claim 23,wherein the device is a hand-held device.
 37. The device of claim 23,wherein the device is an implantable medical device.
 38. The device ofclaim 37, wherein the implantable medical device is one of a pacemaker,an implantable cardioverter (ICD), an implantable loop recorder, and animplantable cardiac monitor.
 39. The device of claim 23, wherein thefirst and second electrodes form a portion of at least one implantablemedical electrical lead.
 40. The device of claim 23, wherein the devicecomprises a hand-held device configured to acquire the ECG from thepatient and a computer configured to process the ECG to determinewhether the patient has atrial fibrillation.
 41. The device of claim 40,wherein the computer is a remote computer.